The Elasticity and Plasticity in Semi-Containerized Co-locating Cloud Workload

Liu, Qixiao; Yu, Zhibin

doi:10.1145/3267809.3267830

Cited by 87 publications

(31 citation statements)

References 27 publications

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“…Particularly, those containers severely under-utilize resources: the real usage of CPU and disk on average is no more than 16% of the requested value. This finding can be also corroborated from other works [5] [6] indicating low resource usage is a systemic issue in large-scale clusters. Under-utilization is resultant of resource fragmentation and user overestimation.…”

Section: Cluster Utilization Issuessupporting

confidence: 88%

Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters

Yang

Sun

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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It is a long-standing challenge to achieve a high degree of resource utilization in cluster scheduling. Resource oversubscription has become a common practice in improving resource utilization and cost reduction. However, current centralized approaches to oversubscription suffer from the issue with resource mismatch and fail to take into account other performance requirements, e.g., tail latency. In this paper we present ROSE, a new resource management platform capable of conducting performance-aware resource oversubscription. ROSE allows latency-sensitive long-running applications (LRAs) to co-exist with computation-intensive batch jobs. Instead of waiting for resource allocation to be confirmed by the centralized scheduler, job managers in ROSE can independently request to launch speculative tasks within specific machines according to their suitability for oversubscription. Node agents of those machines can however avoid any excessive resource oversubscription by means of a mechanism for admission control using multi-resource threshold control and performance-aware resource throttle. Experiments show that in case of mixed co-location of batch jobs and latency-sensitive LRAs, the CPU utilization and the disk utilization can reach 56.34% and 43.49%, respectively, but the 95th percentile of read latency in YCSB workloads only increases by 5.4% against the case of executing the LRAs alone.

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Section: Cluster Utilization Issuessupporting

confidence: 88%

Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters

Yang

Sun

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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“…Lu et al [13] performed a characterization of the Alibaba cloud trace and disclosed four types of imbalance (i.e., spatial imbalance, temporal imbalance, proportion imbalance of resources utilization per workload, and resource demands and runtime statistics imbalance). The work in [14] investigates the elasticity and plasticity of resource allocation of the Alibaba trace. The authors in [15] focused on providing a unique and microscopic view about how the co-located workloads interact and impact each other.…”

Section: Datasetmentioning

confidence: 99%

Detection of SLA Violation for Big Data Analytics Applications in Cloud

Zeng

Garg

Barika

et al. 2021

IEEE Trans. Comput.

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SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this paper, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.

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“…Although there are research efforts on traditional workload characterization, server consolidation and VM migration in data centers, workload characterization on co-allocated jobs in data centers is rare. There are some works on analysis of Alibaba's trace data [54]- [59]. They focus on imbalance phenomena in the cloud.…”

Section: Related Workmentioning

confidence: 99%

Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud

et al. 2019

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In order to reduce power and energy costs, giant cloud providers now mix online and batch jobs on the same cluster. Although the co-allocation of such jobs improves machine utilization, it challenges the data center scheduler and workload assignment in terms of quality of service, fault tolerance, and failure recovery, especially for latency critical online services. In this paper, we explore various characteristics of co-allocated online services and batch jobs from a production cluster containing 1.3k servers in Alibaba Cloud. From the trace data, we find the following: 1) For batch jobs with multiple tasks and instances, 50.8% failed tasks wait and halted after a very long time interval when their first and the only one instance fails. This wastes much time and resources as the remaining instances are running for an impossible successful termination. 2) For online services jobs, they are clustered in 25 categories according to their requested CPU, memory, and disk resources. Such clustering can help the co-allocation of online services jobs with batch jobs. 3) Servers are clustered into seven groups by CPU utilization, memory utilization, and their correlations. Machines with a strong correlation between CPU and memory utilization provides an opportunity for job co-allocation and resource utilization estimation. 4) The MTBF (mean time between failures) of instances are in the interval [400, 800] seconds while the average completion time of the 99th percentile is 1003 seconds. We also compare the cumulative distribution functions of jobs and servers and explain the differences and opportunities for workload assignment between them. Our findings and insights presented in this paper can help the community and data center operators better understand the workload characteristics, improve resource utilization, and failure recovery design. INDEX TERMS Co-allocated jobs, workload characterization, online services, batch jobs, data center, scheduling.

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The Elasticity and Plasticity in Semi-Containerized Co-locating Cloud Workload

Cited by 87 publications

References 27 publications

Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters

Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters

Detection of SLA Violation for Big Data Analytics Applications in Cloud

Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud

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